code: def test_distributed_bleu(self):
      predictions = [
          torch.tensor([[1, 0, 0], [1, 1, 0]]),
          torch.tensor([[1, 1, 1]]),
      ]
      gold_targets = [
          torch.tensor([[2, 0, 0], [1, 0, 0]]),
          torch.tensor([[1, 1, 2]]),
      ]
  
      check = math.exp(
          0.5 * (math.log(3) - math.log(6))
          + 0.5 * (math.log(1) - math.log(3))
      )
      metric_kwargs = {
          "predictions": predictions,
          "gold_targets": gold_targets,
      }
      desired_values = {"BLEU": check}
      run_distributed_test(
          [-1, -1],
          global_distributed_metric,
          BLEU(ngram_weights=(0.5, 0.5), exclude_indices={0}),
          metric_kwargs,
          desired_values,
          exact=False,
      )
  
file_path: allennlp/tests/training/metrics/bleu_test.py
imports: from typing import Any, Dict, List, Tuple, Union
  import math
  from collections import Counter
  import torch
  from torch.testing import assert_allclose
  from allennlp.common.testing import AllenNlpTestCase, multi_device, global_distributed_metric, run_distributed_test
  from allennlp.training.metrics import BLEU
  from allennlp.training.util import ngrams, get_valid_tokens_mask
label_desc: Use image node_id 6 for calling the BleuTest obj's underlying member method code with example usage: obj.test_distributed_bleu() without return types